A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient, and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition.

翻译：合理均衡的饮食对维持身体健康至关重要。随着深度学习的进步，基于食物图像的自动化营养估算方法为监测日常营养摄入和促进膳食健康提供了有前景的解决方案。尽管基于单目图像的营养估算在便捷性、高效性和经济性方面具有优势，但其精度受限的问题仍是重大挑战。针对这一问题，我们提出了DPF-Nutrition，一种采用单目图像的端到端营养估算方法。在所提出的DPF-Nutrition方法中，我们引入深度预测模块生成深度图，从而提升食物份量估算的准确性。此外，我们设计了一个RGB-D融合模块，通过结合单目图像与预测获得的深度信息，显著改善了营养估算性能。据我们所知，这是首个将深度预测和RGB-D融合技术应用于食物营养估算领域的研究工作。在Nutrition5k数据集上开展的综合实验验证了DPF-Nutrition方法的效果与效率。